Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink

Jonas Elsborg, Felix Aertebjerg, Luca Thiede, Alan Aspuru-Guzik, Tejs Vegge, Arghya Bhowmik

Keywords: charge density, gnn, periodic, materials, crystals, dft

TL;DR: ultra-fast charge density prediction in periodic systems

Abstract: We introduce ELECTRAFI, a fast, end-to-end differentiable model for predicting periodic charge densities in crystalline materials. ELECTRAFI constructs anisotropic Gaussians in real space and exploits their closed-form Fourier transforms to analytically evaluate plane-wave coefficients via the Poisson summation formula. This formulation delegates non-local and periodic behavior to analytic transforms, enabling reconstruction of the full periodic charge density with a single inverse FFT. By avoiding explicit real-space grid probing, periodic image summation, and spherical harmonic expansions, ELECTRAFI matches or exceeds state-of-the-art accuracy across periodic benchmarks while being up to $633\times$ faster than the strongest competing method, reconstructing crystal charge densities in a fraction of a second. When used to initialize DFT calculations, ELECTRAFI reduces total DFT compute cost by up to $\sim$20 \%, whereas slower models negate savings due to high inference times. Our results show that accuracy and inference cost jointly determine end-to-end DFT speedups, and motivate our focus on efficiency.

Submission Track: Full Paper

Submission Category: AI-Guided Design

Submission Number: 6